Computer Vision

Researchers

Awards

AWARD Best Paper - Honorable Mention Award at WACV 2021
Date: January 6, 2021
Awarded to: Rushil Anirudh, Suhas Lohit, Pavan Turaga
MERL Contact: Suhas Lohit
Research Areas: Computational Sensing, Computer Vision, Machine Learning
Brief
- A team of researchers from Mitsubishi Electric Research Laboratories (MERL), Lawrence Livermore National Laboratory (LLNL) and Arizona State University (ASU) received the Best Paper Honorable Mention Award at WACV 2021 for their paper "Generative Patch Priors for Practical Compressive Image Recovery".
  
  The paper proposes a novel model of natural images as a composition of small patches which are obtained from a deep generative network. This is unlike prior approaches where the networks attempt to model image-level distributions and are unable to generalize outside training distributions. The key idea in this paper is that learning patch-level statistics is far easier. As the authors demonstrate, this model can then be used to efficiently solve challenging inverse problems in imaging such as compressive image recovery and inpainting even from very few measurements for diverse natural scenes.
AWARD MERL Researchers win Best Paper Award at ICCV 2019 Workshop on Statistical Deep Learning in Computer Vision
Date: October 27, 2019
Awarded to: Abhinav Kumar, Tim K. Marks, Wenxuan Mou, Chen Feng, Xiaoming Liu
MERL Contact: Tim K. Marks
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
Brief
- MERL researcher Tim Marks, former MERL interns Abhinav Kumar and Wenxuan Mou, and MERL consultants Professor Chen Feng (NYU) and Professor Xiaoming Liu (MSU) received the Best Oral Paper Award at the IEEE/CVF International Conference on Computer Vision (ICCV) 2019 Workshop on Statistical Deep Learning in Computer Vision (SDL-CV) held in Seoul, Korea. Their paper, entitled "UGLLI Face Alignment: Estimating Uncertainty with Gaussian Log-Likelihood Loss," describes a method which, given an image of a face, estimates not only the locations of facial landmarks but also the uncertainty of each landmark location estimate.
AWARD CVPR 2011 Longuet-Higgins Prize
Date: June 25, 2011
Awarded to: Paul A. Viola and Michael J. Jones
Awarded for: "Rapid Object Detection using a Boosted Cascade of Simple Features"
Awarded by: Conference on Computer Vision and Pattern Recognition (CVPR)
MERL Contact: Michael J. Jones
Research Area: Machine Learning
Brief
- Paper from 10 years ago with the largest impact on the field: "Rapid Object Detection using a Boosted Cascade of Simple Features", originally published at Conference on Computer Vision and Pattern Recognition (CVPR 2001).

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News & Events

NEWS Suhas Lohit presents invited talk at Boston Symmetry Day 2025
Date: March 31, 2025
Where: Northeastern University, Boston, MA
MERL Contact: Suhas Lohit
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
Brief
- MERL researcher Suhas Lohit was an invited speaker at Boston Symmetry Day, held at Northeastern University. Boston Symmetry Day, an annual workshop organized by researchers at MIT and Northeastern, brought together attendees interested in symmetry-informed machine learning and its applications. Suhas' talk, titled “Efficiency for Equivariance, and Efficiency through Equivariance” discussed recent MERL works that show how to build general and efficient equivariant neural networks, and how equivariance can be utilized in self-supervised learning to yield improved 3D object detection. The abstract and slides can be found in the link below.
TALK [MERL Seminar Series 2025] Petar Veličković presents talk titled Amplifying Human Performance in Combinatorial Competitive Programming
Date & Time: Wednesday, February 26, 2025; 11:00 AM
Speaker: Petar Veličković, Google DeepMind
MERL Host: Anoop Cherian
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
Abstract
- Recent years have seen a significant surge in complex AI systems for competitive programming, capable of performing at admirable levels against human competitors. While steady progress has been made, the highest percentiles still remain out of reach for these methods on standard competition platforms such as Codeforces. In this talk, I will describe and dive into our recent work, where we focussed on combinatorial competitive programming. In combinatorial challenges, the target is to find as-good-as-possible solutions to otherwise computationally intractable problems, over specific given inputs. We hypothesise that this scenario offers a unique testbed for human-AI synergy, as human programmers can write a backbone of a heuristic solution, after which AI can be used to optimise the scoring function used by the heuristic. We deploy our approach on previous iterations of Hash Code, a global team programming competition inspired by NP-hard software engineering problems at Google, and we leverage FunSearch to evolve our scoring functions. Our evolved solutions significantly improve the attained scores from their baseline, successfully breaking into the top percentile on all previous Hash Code online qualification rounds, and outperforming the top human teams on several. To the best of our knowledge, this is the first known AI-assisted top-tier result in competitive programming.

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Research Highlights

Internships

ST0096: Internship - Multimodal Tracking and Imaging

MERL is seeking a motivated intern to assist in developing hardware and algorithms for multimodal imaging applications. The project involves integration of radar, camera, and depth sensors in a variety of sensing scenarios. The ideal candidate should have experience with FMCW radar and/or depth sensing, and be fluent in Python and scripting methods. Familiarity with optical tracking of humans and experience with hardware prototyping is desired. Good knowledge of computational imaging and/or radar imaging methods is a plus.
Required Specific Experience
- Experience with Python and Python Deep Learning Frameworks.
- Experience with FMCW radar and/or Depth Sensors.
CV0075: Internship - Multimodal Embodied AI

MERL is looking for a self-motivated intern to work on problems at the intersection of multimodal large language models and embodied AI in dynamic indoor environments. The ideal candidate would be a PhD student with a strong background in machine learning and computer vision, as demonstrated by top-tier publications. The candidate must have prior experience in designing synthetic scenes (e.g., 3D games) using popular graphics software, embodied AI, large language models, reinforcement learning, and the use of simulators such as Habitat/SoundSpaces. Hands on experience in using animated 3D human shape models (e.g., SMPL and variants) is desired. The intern is expected to collaborate with researchers in computer vision at MERL to develop algorithms and prepare manuscripts for scientific publications.
Required Specific Experience
- Experience in designing 3D interactive scenes
- Experience with vision based embodied AI using simulators (implementation on real robotic hardware would be a plus).
- Experience training large language models on multimodal data
- Experience with training reinforcement learning algorithms
- Strong foundations in machine learning and programming
- Strong track record of publications in top-tier computer vision and machine learning venues (such as CVPR, NeurIPS, etc.).
CV0101: Internship - Multimodal Algorithmic Reasoning

MERL is looking for a self-motivated intern to research on problems at the intersection of multimodal large language models and neural algorithmic reasoning. An ideal intern would be a Ph.D. student with a strong background in machine learning and computer vision. The candidate must have prior experience with training multimodal LLMs for solving vision-and-language tasks. Experience in participating and winning mathematical Olympiads is desired. Publications in theoretical machine learning venues would be a strong plus. The intern is expected to collaborate with researchers in the computer vision team at MERL to develop algorithms and prepare manuscripts for scientific publications.
Required Specific Experience
- Experience with training large vision-and-language models
- Experience with solving mathematical reasoning problems
- Experience with programming in Python using PyTorch
- Enrolled in a PhD program
- Strong track record of publications in top-tier computer vision and machine learning venues (such as CVPR, NeurIPS, etc.).

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Openings

CI0130: Postdoctoral Research Fellow - Artificial General Intelligence (AGI)

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Recent Publications

Singh, A., Jones, M.J., Peng, K.-C., Chatterjee, M., Cherian, A., Learned-Miller, E., "Improving Open-World Object Localization by Discovering Background", CVPR Workshop on Domain Generalization: Evolution, Breakthroughs and Future Horizon, May 2025.
BibTeX TR2025-058 PDF
- @inproceedings{Singh2025may,
- author = {Singh, Ashish and Jones, Michael J. and Peng, Kuan-Chuan and Chatterjee, Moitreya and Cherian, Anoop and Learned-Miller, Erik},
- title = {{Improving Open-World Object Localization by Discovering Background}},
- booktitle = {CVPR Workshop on Domain Generalization: Evolution, Breakthroughs and Future Horizon},
- year = 2025,
- month = may,
- url = {https://www.merl.com/publications/TR2025-058}
- }
Basu, S., Lohit, S., Brand, M., "G-RepsNet: A Lightweight Construction of Equivariant Net- works for Arbitrary Matrix Groups", Transactions on Machine Learning Research (TMLR), May 2025.
BibTeX TR2025-056 PDF Software
- @article{Basu2025may,
- author = {Basu, Sourya and Lohit, Suhas and Brand, Matthew},
- title = {{G-RepsNet: A Lightweight Construction of Equivariant Net- works for Arbitrary Matrix Groups}},
- journal = {Transactions on Machine Learning Research (TMLR)},
- year = 2025,
- month = may,
- url = {https://www.merl.com/publications/TR2025-056}
- }
Tang, H., Ellis, K., Lohit, S., Jones, M.J., Chatterjee, M., "Programmatic Video Prediction Using Large Language Models", International Conference on Learning Representations Workshops (ICLRW), April 2025.
BibTeX TR2025-049 PDF
- @inproceedings{Tang2025apr,
- author = {Tang, Hao and Ellis, Kevin and Lohit, Suhas and Jones, Michael J. and Chatterjee, Moitreya},
- title = {{Programmatic Video Prediction Using Large Language Models}},
- booktitle = {International Conference on Learning Representations Workshops (ICLRW)},
- year = 2025,
- month = apr,
- url = {https://www.merl.com/publications/TR2025-049}
- }
Hori, C., Kambara, M., Sugiura, K., Ota, K., Khurana, S., Jain, S., Corcodel, R., Jha, D.K., Romeres, D., Le Roux, J., "Interactive Robot Action Replanning using Multimodal LLM Trained from Human Demonstration Videos", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), April 2025.
BibTeX TR2025-034 PDF
- @inproceedings{Hori2025mar,
- author = {Hori, Chiori and Kambara, Motonari and Sugiura, Komei and Ota, Kei and Khurana, Sameer and Jain, Siddarth and Corcodel, Radu and Jha, Devesh K. and Romeres, Diego and {Le Roux}, Jonathan},
- title = {{Interactive Robot Action Replanning using Multimodal LLM Trained from Human Demonstration Videos}},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2025,
- month = mar,
- url = {https://www.merl.com/publications/TR2025-034}
- }
Ranade, S., Pais, G., Whitaker, R., Nascimento, J., Miraldo, P., Ramalingam, S., "SurfR: Surface Reconstruction with Multi-scale Attention", International Conference on 3D Vision (3DV), March 2025.
BibTeX TR2025-039 PDF Presentation
- @inproceedings{Ranade2025mar,
- author = {{{Ranade, Siddhant and Pais, Goncalo and Whitaker, Ross and Nascimento, Jacinto and Miraldo, Pedro and Ramalingam, Srikumar}}},
- title = {{{SurfR: Surface Reconstruction with Multi-scale Attention}}},
- booktitle = {International Conference on 3D Vision (3DV)},
- year = 2025,
- month = mar,
- url = {https://www.merl.com/publications/TR2025-039}
- }
Wang, Y., Peng, K.-C., Fu, R., "Towards Zero-shot 3D Anomaly Localization", IEEE Winter Conference on Applications of Computer Vision (WACV), Biswas, S. and Averbuch-Elor, H. and Štruc, V. and Yang, Y., Eds., DOI: 10.1109/WACV61041.2025.00148, February 2025, pp. 1447-1456.
BibTeX TR2025-020 PDF Video Presentation
- @inproceedings{Wang2025feb2,
- author = {Wang, Yizhou and Peng, Kuan-Chuan and Fu, Raymond},
- title = {{Towards Zero-shot 3D Anomaly Localization}},
- booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
- year = 2025,
- editor = {Biswas, S. and Averbuch-Elor, H. and Štruc, V. and Yang, Y.},
- pages = {1447--1456},
- month = feb,
- publisher = {IEEE},
- doi = {10.1109/WACV61041.2025.00148},
- issn = {2642-9381},
- isbn = {979-8-3315-1083-1},
- url = {https://www.merl.com/publications/TR2025-020}
- }
Mumcu, F., Jones, M.J., Yilmaz, Y., Cherian, A., "ComplexVAD: Detecting Interaction Anomalies in Video", IEEE Winter Conference on Applications of Computer Vision (WACV) Workshop, February 2025.
BibTeX TR2025-016 PDF
- @inproceedings{Mumcu2025feb,
- author = {Mumcu, Furkan and Jones, Michael J. and Yilmaz, Yasin and Cherian, Anoop},
- title = {{ComplexVAD: Detecting Interaction Anomalies in Video}},
- booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV) Workshop},
- year = 2025,
- month = feb,
- url = {https://www.merl.com/publications/TR2025-016}
- }
Lohit, S., Marks, T.K., "Rotation-Equivariant Neural Networks for Cloud Removal from Satellite Images", Asilomar Conference on Signals, Systems, and Computers (ACSSC), DOI: 10.1109/IEEECONF60004.2024.10942613, January 2025, pp. 1360-1365.
BibTeX TR2025-009 PDF
- @inproceedings{Lohit2025jan,
- author = {Lohit, Suhas and Marks, Tim K.},
- title = {{Rotation-Equivariant Neural Networks for Cloud Removal from Satellite Images}},
- booktitle = {2024 58th Asilomar Conference on Signals, Systems, and Computers (ACSSC)},
- year = 2025,
- pages = {1360--1365},
- month = jan,
- publisher = {IEEE},
- doi = {10.1109/IEEECONF60004.2024.10942613},
- issn = {2576-2303},
- isbn = {979-8-3503-5405-8},
- url = {https://www.merl.com/publications/TR2025-009}
- }